1. Keystroke Biometric : ROC Experiments Team Abhishek Kanchan Priyanka Ranadive Sagar Desai Pooja Malhotra Ning Wang

2. Keystroke Biometric : ROC Experiments WHAT IS KEYSTROKE BIOMETRIC ? The keystroke biometric is one of the less- studied behavioral biometrics. Keystroke biometric systems measure typing characteristics believed to be unique to an individual and difficult to duplicate. Used for Identification Used for Authentication Developed over the past 6+ years

3. Keystroke Biometric : ROC Experiments Introduction to ROC Curves  Used for binary decisions  Signal detection – signal / no signal  Medical diagnosis – disease / no disease  Biometric authentication – you are the person you claim to be / you are not

4. Keystroke Biometric : ROC Experiments Introduction to ROC Curves  In biometrics the ROC curve varies from FAR=1 & FRR=0 at one end to FAR=0 & FRR=1 at other  FAR = False Accept Rate – the rate an imposter is falsely accepted  FRR = False Reject Rate – the rate the correct person is falsely rejected  ROC Charts are expressed in terms of percentages (0- 100%) or probabilities (0-1). These are used interchangeably.

5. Keystroke Biometric : ROC Experiments ROC Authentication Analogy Supreme Court – nine judges – Usual procedure – majority required to make decision – Like 9NN needing majority to authenticate a user ROC Curve – effectively creates many potential procedures and provides FAR/FRR tradeoff for each (here is the m-kNN method) – Need 9 votes to make decision (very conservative) – Need 8, 7, 6 votes to make decision (conservative) – Need 5 votes to make decision (majority) – Need 4, 3, 2 votes to make decision (liberal) – Need 1 or even 0 votes to make decision (very liberal)

6. Keystroke Biometric : ROC Experiments ROC EXPERIMENTS Derived from four nonparametric techniques. ‘Weak' and ‘Strong' training experiments. – Weak Enrollment data, only non-test- subject data is used to train the system. – Strong enrollment uses test-subject data to train the system, and then uses independent (different) test-subject data to test the system. Large Data Experiments

7. Keystroke Biometric : ROC Experiments SYSTEM OVERVIEW

8. Keystroke Biometric : ROC Experiments Parametric Procedures  Parametric techniques are well studied.  Data follows a normal or Gaussian distribution.  Vary a threshold to obtain the tradeoff between FAR/FRR.  Probability density functions can be calculated without estimation. Parametric ROC - Probability Density Function - Adapted from Cha, et al (2009)

9. Keystroke Biometric : ROC Experiments Cha Dichotomy Model  Simplifies complexity  Transforms a feature space into a distance vector space.  Uses distance measures. Multi-class to two Class Transformation Process, Adapted from Yoon et al (2005)

10. Keystroke Biometric : ROC Experiments Pure Rank Method – m-kNN  Pure Rank Method.  Evaluate the top 7 NN.  Q is authenticated if # within-class matches is >= decision threshold of 4NN.  Unweighted. All W’s are equal in weight.

11. Keystroke Biometric : ROC Experiments Rank Method Weighted by Rank Order wm-kNN  Authenticate if W choices are > weighted match (m)  Score varies from 0 to =k(k+1)/2  For every m, FAR/FRR pair or ROC point.  If m=0, FAR=1, FAR=0 …All users accepted.  If m=15, FAR=small, FRR=large, few Q’s accepted.

12. Keystroke Biometric : ROC Experiments m-kNN and wm-kNN ROC’s LapFree – Weak Training

13. Keystroke Biometric : ROC Experiments Distance Threshold Method t-kNN  A positive vote is within a distance threshold from the user’s sample.  Uses feature vector space distances only.  At 0, no distance vectors are authenticated. FAR=0, FRR=100%. At t=100, all distance vectors are authenticated. FAR=100, FRR=0.

14. Keystroke Biometric : ROC Experiments Threshold (t-kNN) Method DeskFree (left) and LapFree (right) Data

15. Keystroke Biometric : ROC Experiments Threshold (ht-kNN) Method  Weighted vote based on distances to the kNN.  Hybrid of rank method and vector space distances.  For each test sample, the within- class weight (WCW) is calculated based on the distance vectors. DeskFree (left) and LapFree (right) Data

16. Keystroke Biometric : ROC Experiments Weak & Strong Training

17. Keystroke Biometric : ROC Experiments DELIVERABLE Deliverable 5 – Authentication Experiments – Ideal Conditions/ Weak Enrollment Part I Status – Completed Deliverable 6 - Authentication Experiments – Ideal Conditions/ Weak Enrollment Part II Status – Completed Deliverable 7 – Enhance and Correct Refactor-BAS.jar ROC interface Status - Completed

18. Keystroke Biometric : ROC Experiments DELIVERABLE 7 Implement Perl ROC with threshold logic in JAVA. Unify the code in Java which was supported by a Perl program earlier for calculating ROC threshold Values. Maintain the performance of Perl code in Java. Some changes in User Interface of ROC program.

19. Keystroke Biometric : ROC Experiments UI CHANGES

20. Keystroke Biometric : ROC Experiments

22. TEAM COMMUNICATION Google Group for information sharing and discussion Skype Meetings Emails Personal Meetings Documented Minutes of Meeting Team Website status updates Assigned Task progress check by team leader

23. Keystroke Biometric : ROC Experiments Questions?